AI helps businesses make sense of large data sets in less time. But it is not without its disadvantages.
For one, it can lead to unemployment because AI can replace some traditional job roles. It can also amplify bias and discrimination if not designed with fairness in mind. And it is prone to cyber attacks.
1. It is prone to error
AI reduces the amount of human error in data processing, analytics and assembly in manufacturing. It also eliminates manual errors in cybersecurity and can help automate tasks, increasing productivity and efficiency. However, even the best AI can fail from time to time.
The biggest source of errors in AI stems from the fact that a significant percentage of the data it relies on is sourced by humans. And as humans are notorious for their irrationality and subjectivity, this effectively transfers to the AI system.
The most common type of error in AI is the false positive, which occurs when the machine misidentifies an object or scenario as a threat that it really is not. A good example of this happened when a shuttle bus stopped in front of a delivery truck that was driving on a lane perpendicular to the bus’s path and did not sound its horn (O’Kane 2019). In this case, the AI system incorrectly identified the vehicle as a potential threat. AI Is More Fun Now, But Not For Everyone.
2. It is biased
AI is an incredible tool, but it can also encode historical human biases and accelerate and amplify biased or flawed decision-making. This can cause a variety of issues, from limiting access to essential services for marginalized communities to reinforcing existing power dynamics.
Currently, human beings choose the data that algorithms use and can introduce bias in the process. Extensive testing and diverse teams can act as effective safeguards against bias, but even with these measures in place, biased outputs can still occur.
This can affect various groups, from women to people of color. For example, facial recognition algorithms based on training data that consists primarily of men can struggle to recognize women and can perpetuate gender stereotypes in security systems. Bias in AI can also contribute to widening socioeconomic inequality by affecting workers who perform lower-wage and less-sophisticated tasks. This is especially concerning, as these jobs are among the first to be automated by AI.
3. It is expensive
The cost of implementing AI in your business can be high. This includes R&D, software and hardware costs, integrating the technology, and testing it before deployment. It can also include the cost of hiring competent professionals such as data scientists and AI developers, which can add up to a significant sum.
The costs of AI are also growing due to the increasing complexity and performance requirements. This is especially true for large language models that require massive computing power to execute each time they return a result.
While there are numerous benefits of artificial intelligence, it’s important to remember that it isn’t inherently good or bad. It’s all about how it is developed and used, and how we can work towards creating a better society for everyone. We can do this by ensuring that AI is designed and programmed with ethical considerations in mind. By doing so, we can ensure that the benefits of AI are widely distributed and minimize any potential risks.
4. It is vulnerable to cyber attacks
As AI becomes more sophisticated and powerful, it becomes vulnerable to cyber attacks. Malicious actors are able to manipulate these systems to cause serious damage. A breach of an AI system could impact people’s physical safety, such as if the AI in a self-driving car suffered a hack. It could also affect a company’s reputation and lead to financial losses.
Creating an AI requires huge amounts of data. This training data often comes with “bias,” or inherent assumptions and preferences. This can skew the results and limit the scope of what an AI model can do.
These biases can be exacerbated by a lack of diverse training data, which leaves the AI more narrow in its perspective. This can lead to discrimination and a lack of fairness. For instance, predictive policing algorithms have been shown to disproportionately target Black communities. This can lead to racial tensions and even civil unrest. Moreover, hackers can use data manipulation techniques like poisoning to alter a model’s decision making.